{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a39167f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys,copy,os,inspect\n",
    "if hasattr(sys.modules[__name__], '__file__'):\n",
    "    _file_name = __file__\n",
    "else:\n",
    "    _file_name = inspect.getfile(inspect.currentframe())\n",
    "CURRENT_FILE_PATH = os.path.dirname(_file_name)\n",
    "sys.path.append(os.getcwd()+\"/../neuronVis\")\n",
    "from HierachicalAnalysis import HieracyAnalysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ebaa9f02",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = HieracyAnalysis()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "020fe692",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ae0b45c5e14a49d1b687996172ea9bb4",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/8122 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "not in up boundary\n",
      "not in up boundary\n",
      "not in up boundary\n",
      "not in up boundary\n",
      "not in up boundary\n",
      "not in up boundary\n",
      "not in up boundary\n",
      "not in up boundary\n",
      "not in up boundary\n",
      "not in up boundary\n",
      "not in up boundary\n",
      "not in up boundary\n",
      "not in up boundary\n",
      "not in up boundary\n",
      "not in up boundary\n",
      "not in up boundary\n",
      "not in up boundary\n",
      "not in up boundary\n",
      "not in up boundary\n",
      "not in up boundary\n",
      "not in up boundary\n",
      "not in up boundary\n",
      "not in up boundary\n",
      "not in up boundary\n",
      "not in up boundary\n",
      "not in up boundary\n",
      "not in up boundary\n",
      "not in up boundary\n",
      "not in up boundary\n",
      "not in up boundary\n",
      "not in up boundary\n",
      "not in up boundary\n",
      "not in up boundary\n",
      "not in up boundary\n",
      "not in up boundary\n",
      "not in up boundary\n",
      "not in up boundary\n",
      "not in up boundary\n",
      "not in up boundary\n",
      "not in up boundary\n",
      "not in up boundary\n",
      "not in up boundary\n",
      "not in up boundary\n",
      "not in up boundary\n",
      "not in up boundary\n",
      "not in up boundary\n",
      "not in up boundary\n",
      "not in up boundary\n",
      "not in up boundary\n",
      "not in up boundary\n",
      "not in up boundary\n",
      "not in up boundary\n",
      "not in up boundary\n",
      "not in up boundary\n",
      "not in up boundary\n",
      "not in up boundary\n",
      "not in up boundary\n",
      "not in up boundary\n",
      "not in up boundary\n",
      "not in up boundary\n",
      "not in up boundary\n",
      "not in up boundary\n",
      "not in up boundary\n",
      "not in up boundary\n",
      "not in up boundary\n",
      "not in up boundary\n",
      "not in up boundary\n",
      "not in up boundary\n",
      "not in up boundary\n",
      "not in up boundary\n",
      "not in up boundary\n",
      "not in up boundary\n",
      "not in up boundary\n",
      "not in up boundary\n",
      "not in up boundary\n",
      "not in up boundary\n",
      "not in up boundary\n",
      "not in up boundary\n",
      "not in up boundary\n",
      "not in up boundary\n",
      "not in up boundary\n",
      "not in up boundary\n",
      "not in up boundary\n",
      "not in up boundary\n",
      "not in up boundary\n",
      "not in up boundary\n",
      "not in up boundary\n",
      "not in up boundary\n",
      "not in up boundary\n",
      "not in up boundary\n",
      "not in up boundary\n",
      "not in up boundary\n",
      "not in up boundary\n",
      "not in up boundary\n",
      "not in up boundary\n",
      "not in up boundary\n",
      "not in up boundary\n",
      "not in up boundary\n",
      "not in up boundary\n",
      "not in up boundary\n",
      "not in up boundary\n",
      "not in up boundary\n",
      "not in up boundary\n",
      "not in up boundary\n",
      "not in up boundary\n",
      "not in up boundary\n",
      "not in up boundary\n",
      "not in up boundary\n",
      "not in up boundary\n",
      "not in up boundary\n",
      "not in up boundary\n",
      "not in up boundary\n",
      "not in up boundary\n",
      "not in up boundary\n",
      "not in up boundary\n",
      "not in up boundary\n"
     ]
    }
   ],
   "source": [
    "'''\n",
    "    Load data or generate data, when generating data, it will automatically download and parse information.\n",
    "'''\n",
    "model.generate_data()\n",
    "#model.load_data('../resource/X.npy','../resource/X_num.npy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "25436f91",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/dell/CX/packages_cache/neuron-vis/figures/../neuronVis/HierachicalAnalysis.py:229: RuntimeWarning: invalid value encountered in true_divide\n",
      "  FF_FB_seperate_index: an index to indicate [0-index) as FF, others as FB\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#use paper's patterns\n",
    "_ = model.get_hierachical_score(0,save_path='_')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "2f1733b9",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/dell/CX/packages_cache/neuron-vis/figures/../neuronVis/HierachicalAnalysis.py:229: RuntimeWarning: invalid value encountered in true_divide\n",
      "  FF_FB_seperate_index: an index to indicate [0-index) as FF, others as FB\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 288x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#use paper's patterns with updating\n",
    "_ = model.get_hierachical_score(1,save_path='_')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "7a22b97c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/dell/CX/packages_cache/neuron-vis/figures/../neuronVis/HierachicalAnalysis.py:229: RuntimeWarning: invalid value encountered in true_divide\n",
      "  FF_FB_seperate_index: an index to indicate [0-index) as FF, others as FB\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#generate patterns\n",
    "_ = model.get_hierachical_score(2,save_path='_')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "e3d097c5",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/dell/CX/packages_cache/neuron-vis/figures/../neuronVis/HierachicalAnalysis.py:229: RuntimeWarning: invalid value encountered in true_divide\n",
      "  FF_FB_seperate_index: an index to indicate [0-index) as FF, others as FB\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#generate patterns with updating\n",
    "_ = model.get_hierachical_score(3,save_path='_')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "e61563f1",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/dell/CX/packages_cache/neuron-vis/figures/../neuronVis/HierachicalAnalysis.py:229: RuntimeWarning: invalid value encountered in true_divide\n",
      "  FF_FB_seperate_index: an index to indicate [0-index) as FF, others as FB\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#better reward\n",
    "_ = model.get_hierachical_score(4,save_path='_')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "730648ce",
   "metadata": {},
   "outputs": [],
   "source": [
    "'''\n",
    "    save pared data\n",
    "'''\n",
    "model.save('../resource/X.npy','../resource/X_num.npy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "512ccccd",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
